Senior Hardware Engineer, Radar

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.

When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to making a real, positive impact on the driving experience for millions of people. 

As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.

Meet the team:

The hardware sensing team is responsible for the benchmarking, characterizing, and nomination and integration of all sensors. We work cross functionally with systems, perceptions, vehicle integration to ensure the sensors meet Latitude’s ODD requirements, perception requirements, and that it is integrable and reliable in Ford’s series vehicles. We are expected to understand sensing from a first principle approach and to ensure those principles are designable and manufacturable.  We keep a breadth of all sensing technology, sensing vendors, and integration technologies. Members of the team are expected to be highly multidisciplinary, as successful sensor integration requires expert understanding and comfortability of concepts across many disciplines–from hardware and software.

What you’ll do: 

• Lead technical development of radar or lidar sensing systems from early concept, benchmarking, and sourcing through production launch
• Define sensor performance targets, requirements, evaluation criteria, and validation plans based on vehicle, perception, and ODD needs
• Drive sensor benchmarking and technical down-selection across candidate suppliers and technologies
• Lead supplier technical engagement, including requirements reviews, data reviews, architecture discussions, issue resolution, and design maturity assessments
• Guide architecture and tradeoff decisions related to sensing performance, environmental robustness, packaging, thermal constraints, power, diagnostics, manufacturability, and cost
• Work cross-functionally with systems, perception, firmware, validation, vehicle integration, manufacturing, and quality teams to ensure the sensor meets downstream functional and program needs
• Review test data from supplier, bench, track, and vehicle testing; identify performance gaps; determine root cause; and drive corrective actions
• Assess whether supplier evidence is technically credible by reviewing test conditions, assumptions, filtering, sample quality, boundary cases, repeatability, and alignment to sensor physics and system requirements
• Lead technical reviews and challenge unsupported claims, weak correlation, incomplete validation, and conclusions that do not match observed data or first-principles behavior
• Establish technical plans, milestones, risks, and mitigation strategies to keep sensor programs on track toward nomination and launch
• Communicate technical status, open issues, and tradeoffs clearly to engineering leadership and cross-functional partners

What you'll need to succeed:

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 7+ years of relevant experience (or Master's degree and 5+ years of relevant experience, or PhD and 2+ years of relevant experience)
• 8+ years of experience in automotive sensing, with deep hands-on expertise in either radar or lidar and demonstrated technical leadership on complex hardware programs
• Strong first-principles understanding of the relevant sensing modality:
  • Radar: RF fundamentals, range/Doppler/angle estimation, resolution, interference, multipath, ghosting, calibration, synchronization, and environmental effects
  • Lidar: time-of-flight or FMCW fundamentals, ranging accuracy, reflectivity effects, point cloud quality, contamination, weather impacts, optical alignment, calibration, and environmental robustness
• Strong scripting and analysis capability in python, including hands-on use of NumPy, pandas, Jupyter, and similar tools to parse logs, evaluate sensor performance, trend issues, and build repeatable analysis workflows
• Ability to work directly with sensor data and debug artifacts, including supplier logs, raw